In this exercise, we are going to study the coverage of coral and algae measured in percentage in the Great Barrier Reef (Australia) at different times and locations. To do that, we are given two data sets, coral1 and coral2, that contain information on the reef identifiers (REEF_NAME and REEF_ID), the reef locations (SECTOR, SHELF, LATITUDE and LONGITUDE), and the Coverage (in percentage) of Algae and Hard Coral (stored in the variable Groups) for each of the locations and times. You can find the data sets for this exercise in the folder called Data.

In addition to the marks displayed in each question, an additional 15 points have been allocated for assessment of general coding style and overall performance.

Please ensure that the report knits properly and all the R code is visible in the final knitted report.

This is an individual assignment

Question 1: Read the first coral data set (coral1.csv) and store it in a data object called coral1 (1pt). Show the first 2 rows of the data frame (1pt). What are the names of the variables in the data frame (use R to extract the names of the variables) (1pt)?

coral1 <- read.csv(here::here("data/coral1.csv")) 
head(coral1,2)%>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
SECTOR SHELF REEF_NAME REEF_ID SITE_NO LATITUDE LONGITUDE SAMPLE_DATE
CA I LOW ISLANDS REEF 16028S 1 -16.38352 145.5710 1993-06-12
CA I LOW ISLANDS REEF 16028S 2 -16.38648 145.5726 1993-06-12 
names(coral1)
## [1] "SECTOR"      "SHELF"       "REEF_NAME"   "REEF_ID"     "SITE_NO"    
## [6] "LATITUDE"    "LONGITUDE"   "SAMPLE_DATE"

Question 2: Read the second coral data set (coral2.csv) (1pt), store it in a data object called coral2_0 and show the first 2 rows of the data frame (1pt). Transform your data frame coral2_0 into a long format data frame, where the variables Algae and Hard Coral are displayed together in a single variable called Groups, and their values in a variable called Coverage (4pts). Store this new data frame in a data object called coral2 and ensure that coral2 contains the variables REEF_ID, SITE_NO, Groups, and Coverage (1pt). Display the first 2 rows (1pt).

coral2_0 <- read.csv(here::here("data/coral2.csv")) 
head(coral2_0,2)  %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
REEF_ID SITE_NO Algae Hard.Coral
16028S 1 30.64 24.77
16028S 2 35.83 29.91 
coral2 <- coral2_0 %>% 
  pivot_longer(cols = 3:4,
               names_to = "Groups",
               values_to = "Coverage")
head(coral2,2) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
REEF_ID SITE_NO Groups Coverage
16028S 1 Algae 30.64
16028S 1 Hard.Coral 24.77

Question 3: Combine both data frames coral1 and coral2 into a single data frame called coral. In your code, you must specify explicitly the variables that you are using to join both data frames (4pts). Show the first 2 rows of the new combined data frame (1pt).

coral <- left_join(coral2,coral1,by = c("REEF_ID","SITE_NO"))
head(coral,2) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
REEF_ID SITE_NO Groups Coverage SECTOR SHELF REEF_NAME LATITUDE LONGITUDE SAMPLE_DATE
16028S 1 Algae 30.64 CA I LOW ISLANDS REEF -16.38352 145.571 1993-06-12
16028S 1 Algae 30.64 CA I LOW ISLANDS REEF -16.38352 145.571 1995-04-15

Question 4: Continue working with the data frame called coral that you created in Question 3. Please use the information in the variable SAMPLE_DATE to create two new variables Year and Month to store the information on year and month, respectively (2pts). These two new variables must be added into the data frame coral. Which are the years represented in this data set (use inline R code to answer this question) (1pt)?

coral <- coral %>% 
  mutate(Year = year(SAMPLE_DATE),
         Month = month(SAMPLE_DATE))

The years present in datafrram coral are 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018

Question 6: Continue working with the same data frame that you have created in Question 3 called coral. Now select only the reefs that are located in Cairns. To do that, please select the reefs of which the variable SECTOR takes the value of CA and store this information in a new data frame called cairns_sector (1pt). What is the dimension of this newly created data frame (use inline R code to answer) (1pt)? How many unique reefs are there in this data frame (use inline R code to answer) (1pt)? Display the first 2 rows of the data frame (1pt).

cairns_sector <- coral %>% 
  filter(SECTOR == "CA")
head(cairns_sector,2) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
REEF_ID SITE_NO Groups Coverage SECTOR SHELF REEF_NAME LATITUDE LONGITUDE SAMPLE_DATE Year Month
16028S 1 Algae 30.64 CA I LOW ISLANDS REEF -16.38352 145.571 1993-06-12 1993 6
16028S 1 Algae 30.64 CA I LOW ISLANDS REEF -16.38352 145.571 1995-04-15 1995 4

The dimensions of the dataframe cairns_sector are 27014, 12.

There are 11 unique Reefs in the cairns_sector.

Question 8: Using the cairns_sector data frame that you created in Question 6, select only the variables REEF_NAME, Groups, Coverage, and Year, then answer the following: Which is the reef with the highest Coverage mean of Hard Coral in 2017 and what is the corresponding Coverage mean (use dplyr and pipes to produce a table that will allow you to answer the questions) (10pts)?

cairns_sector %>% 
  select(REEF_NAME,Groups,Coverage,Year) %>%
  filter(Groups == "Hard.Coral" & Year == 2017) %>% 
  group_by(REEF_NAME) %>% 
  summarise(Avg.coverage = mean(Coverage)) %>% 
  slice_max(Avg.coverage) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
REEF_NAME Avg.coverage
AGINCOURT REEFS (NO 1) 26.87573

Question 9: Keep working on the data frame cairns_sector from Question 6. Now investigate the differences in the Coverage of Algae and Hard Coral in the variable SHELF. The variable SHELF contains information about the location of the reefs with respect to the coast: I (= inner) represents reefs closer to the shore, M (= mid) represents reefs located in the middle shelf, and O (= outer) represents reefs located in the outer shelf. Select only the years 1993 and 2017 and then create a table using the function kable(), where you display the mean values for Algae and Hard Coral Coverage for each of the different SHELF locations per year.

cairns_sector %>% 
  filter(Year == 1993 | Year == 2017) %>% 
  group_by(Groups,SHELF,Year) %>% 
  summarise(Avg_coverage = mean(Coverage)) %>% 
  arrange(Year,desc(Avg_coverage)) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
Groups SHELF Year Avg_coverage
Algae I 1993 61.21745
Algae M 1993 55.87581
Algae O 1993 42.12000
Hard.Coral M 1993 23.67801
Hard.Coral O 1993 22.44467
Hard.Coral I 1993 15.83418
Algae M 2017 59.31029
Algae O 2017 45.53367
Hard.Coral O 2017 24.66020
Hard.Coral M 2017 22.11563

Question 10: Use the data frame cairns_sector that you created in Question 6. How many observations for each reef are there for algae in year 2000 (3pt)? Which reef is represented with the highest number of observations in the year 2000 (1pt)?

cairns_sector %>% 
  filter(Year == 2000 & Groups == "Algae") %>% 
  group_by(REEF_NAME) %>% 
  summarise(No_of_Observation = n()) %>% 
  arrange(-No_of_Observation) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
REEF_NAME No_of_Observation
HASTINGS REEF 78
AGINCOURT REEFS (NO 1) 75
ST CRISPIN REEF 75
THETFORD REEF 72
GREEN ISLAND REEF 57
MICHAELMAS REEF 57
FITZROY ISLAND REEF 54
LOW ISLANDS REEF 53
MACKAY REEF 51
OPAL (2) 51 
reef_top <- cairns_sector %>% 
  filter(Year == 2000) %>% 
  group_by(REEF_NAME) %>% 
  summarise(No_of_Observation = n()) %>% 
  slice_max(No_of_Observation)

Reef with the highest number of observations in the year 2000 is HASTINGS REEF

Question 11: Use the data frame cairns_mean that you created in Question 7. Here our aim is to understand whether there is a relationship between the Hard Coral Coverage and the Algae Coverage over time. To do that, please transform the data frame cairns_mean into wide format so that we can have two variables Algae and Hard Coral that will contain the corresponding Coverage values. Store the new data frame in a data object called cairns_wider (3pts). Display the first two rows of the data frame cairns_wider (1pt). Using geom_point(), plot the variable Hard Coral on the y axis and Algae on the x axis (2pts). Add a title to the figure that reads “Relationship between Algae and Hard Coral Coverage in the Cairns Sector” (1pt).

cairns_wider <- coral2_0  

cairns_wider %>% 
  ggplot(aes(Algae,
             Hard.Coral)) +
  geom_point() +
  ggtitle("Relationship between Algae and Hard Coral Coverage in the Cairns Sector")

Question 12 5510: Fit two models to the data frame cairns_wider you created in Question 11 and discuss whether any of the model is adequate to explain the relationship between those two variables and why. Provide evidence (using R code) and explanations for your answer (10pts).

tidy(lm(Hard.Coral ~ Algae,data = cairns_wider)) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("basic","striped","hover"))
term estimate std.error statistic p.value
(Intercept) 58.2209691 0.5584786 104.2492 0
Algae -0.5751255 0.0096048 -59.8787 0

Tje Above table clearly states that there is a -ve correlation between the variables Algae and Harrd.Coral. And it also tells that the relation is fairly strong as value is approx -0.6.